# Correlations Introductory Overview - Spurious Correlations

Although you cannot prove causal relations based on correlation coefficients (see Elementary concepts), you can still identify so-called spurious correlations; that is, correlations that are due mostly to the influences of "other" variables. For example, there is a correlation between the total amount of losses in a fire and the number of firemen that were putting out the fire; however, what this correlation does not indicate is that if you call fewer firemen then you would lower the losses. There is a third variable (the initial size of the fire) that influences both the amount of losses and the number of firemen. If you "control" for this variable (e.g., consider only fires of a fixed size), then the correlation will either disappear or perhaps even change its sign. The main problem with spurious correlations is that we typically do not know what the "hidden" agent is. However, in cases when we know where to look, we can use partial correlations that control for (partial out) the influence of specified variables. In STATISTICA, partial correlation procedures are included as part of the Multiple Regression module.